Data Analyst Interview Questions India 2026

 

Quick answer — what questions are asked in Data Analyst Interview Questions India 2026?

Data analyst interviews in India in 2026 typically have 3–5 rounds: aptitude and communication screening, SQL technical round, Python/Excel analytical round, business case and scenario round, and HR/culture fit round.

The 5 questions asked most frequently across all Indian companies:

1. “Write a SQL query to find the top 5 customers by revenue in the last 30 days” (SQL round)
2. “How would you handle missing values in a dataset?” (Python/statistics round)
3. “Walk me through a dashboard you have built” (portfolio round)
4. “What is the difference between GROUP BY and HAVING in SQL?” (conceptual SQL)
5. “How would you explain this data finding to a non-technical manager?” (communication round)

Power BI appears in 72% of analytics roles, Python in 68%, and Tableau in 44% in Indian job postings as of 2026.

Cambridge Infotech Data Analytics Course: +91 9902461116

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Introduction — how Indian data analyst interviews are structured in 2026

Every data analyst interview in India follows a broadly similar structure — regardless of whether you are applying to TCS, Flipkart, HDFC Bank, or a funded Bangalore startup. Understanding this structure before preparing saves weeks of wasted effort.

Most data analyst interviews at Indian companies in 2026 include 3–5 rounds including aptitude, technical, and HR. With consistent preparation of 2–3 months on projects and revision, candidates reach job-readiness within that timeframe.

The five-round structure:

Round 1 — Aptitude / Communication screening: Logical reasoning, basic quantitative aptitude, verbal ability. Some companies (TCS, Infosys, Wipro) use standardised online assessments. Others (product companies, startups) skip directly to technical.

Round 2 — SQL technical round: Writing SQL queries to answer specific business questions. This is the most universally tested round across every Indian data analyst interview — from IT services to banks to e-commerce.

Round 3 — Python/Excel and analytical round: Pandas data manipulation, Excel functions (at companies using Excel heavily), or Jupyter notebook exercises. Business logic applied to data.

Round 4 — Business case and portfolio round: Walking the interviewer through your projects, answering “how would you analyse this business problem?” and demonstrating data storytelling — translating findings into business language.

Round 5 — HR and culture fit: Salary negotiation, career goals, team collaboration scenarios.

This guide covers every round with the actual questions asked at named Indian companies, working code for every technical question, and model answers for every conceptual and business case question.

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Round 1 — Aptitude and screening questions

These questions filter the candidate pool before technical interviews begin. Most companies administer them online.

The 4 types of aptitude questions asked in data analyst screenings

Type 1 — Quantitative aptitude (time, speed, percentage, profit-loss)

Sample: “A product costs ₹800. After a 25% discount and then a 10% tax on the discounted price, what is the final price?”

Answer: Discounted price = ₹800 × 0.75 = ₹600. Tax = ₹600 × 0.10 = ₹60. Final price = ₹660.

For data analyst roles specifically, the aptitude tests emphasise percentage calculations, ratio and proportion, and data interpretation from charts and tables — because these directly reflect the daily work of an analyst.

Type 2 — Data interpretation (reading tables, charts, graphs)

Sample: “A bar chart shows monthly sales for 5 products. Which product had the highest average monthly sales, and by what percentage did it exceed the second-highest?”

These questions specifically test whether a candidate can extract correct values from charts quickly and perform calculations on them. Cambridge Infotech students practice data interpretation using real Indian business datasets — which is why they consistently outperform candidates who only practised textbook problems.

Type 3 — Logical reasoning (pattern recognition, syllogisms, arrangements)

Data analysts deal with pattern recognition constantly. Aptitude tests validate this through seating arrangement problems, coded direction problems, and syllogism questions.

Type 4 — Communication and verbal ability

Reading comprehension and sentence correction. For data analyst roles specifically, interviewers are assessing whether you can write a clear, accurate analysis report — verbal ability predicts this.

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Round 2 — SQL interview questions (the most important technical round)

This is the round that decides most data analyst placements in India. SQL proficiency is non-negotiable — the question is how well you can write queries to answer specific business questions under interview pressure.

Certifications are valued but SQL is non-negotiable. SQL window functions, GROUP BY, subqueries, and JOINs are tested universally.

Conceptual SQL questions

Q1: What is the difference between WHERE and HAVING in SQL?

Model Answer: WHERE filters rows before GROUP BY aggregation — it operates on individual rows. HAVING filters groups after GROUP BY aggregation — it operates on aggregated values.

-- WHERE: filter individual rows before grouping
SELECT department, COUNT(*) as employee_count
FROM employees
WHERE salary > 50000        -- filters rows first
GROUP BY department;

-- HAVING: filter groups after aggregation
SELECT department, COUNT(*) as employee_count
FROM employees
GROUP BY department
HAVING COUNT(*) > 10;       -- filters groups after counting

Why interviewers ask this: Confusing WHERE and HAVING is one of the most common SQL errors. Candidates who answer this confidently signal they understand the SQL execution order — which matters in complex queries.

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Q2: What is the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN?

Model Answer:

• INNER JOIN: Returns only rows where a match exists in both tables
• LEFT JOIN: Returns all rows from the left table, with NULLs where no match in the right table
• RIGHT JOIN: Returns all rows from the right table, with NULLs where no match in the left table
• FULL OUTER JOIN: Returns all rows from both tables, with NULLs where no match on either side

-- LEFT JOIN example: all customers, even those with no orders
SELECT c.customer_name, o.order_id, o.order_value
FROM customers c
LEFT JOIN orders o ON c.customer_id = o.customer_id;
-- Customers with no orders appear with NULL in order columns

When to use each: Data analysts use LEFT JOIN most frequently — “show me all customers and their order history” where customers without orders must still appear.

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Q3: What are SQL window functions and when do you use them?

Model Answer: Window functions perform calculations across a set of rows related to the current row — without collapsing the rows the way GROUP BY does. They are essential for: running totals, ranking, period-over-period comparisons, and moving averages.

-- ROW_NUMBER: assign sequential rank
SELECT 
    product_name,
    revenue,
    ROW_NUMBER() OVER (ORDER BY revenue DESC) AS rank
FROM sales;

-- Running total
SELECT 
    sale_date,
    daily_revenue,
    SUM(daily_revenue) OVER (ORDER BY sale_date) AS running_total
FROM daily_sales;

-- Month-over-month comparison using LAG
SELECT 
    month,
    revenue,
    LAG(revenue, 1) OVER (ORDER BY month) AS prev_month_revenue,
    revenue - LAG(revenue, 1) OVER (ORDER BY month) AS mom_change
FROM monthly_sales;

Window functions are asked in every mid-to-senior data analyst interview. They are tested at TCS Analytics, Infosys, Amazon India, and Flipkart.

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Practical SQL coding questions (write the query)

Q4 — TCS / Infosys: “Write a SQL query to find the top 5 customers by revenue in the last 30 days.”

SELECT 
    c.customer_id,
    c.customer_name,
    SUM(o.order_value) AS total_revenue
FROM customers c
INNER JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY c.customer_id, c.customer_name
ORDER BY total_revenue DESC
LIMIT 5;

Interviewer follow-up: “What if there are ties at position 5?” → Use RANK() window function instead of LIMIT 5 to handle ties correctly.

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Q5 — Flipkart / Amazon India: “Find all customers who placed orders in January but not in February 2026.”

SELECT DISTINCT customer_id
FROM orders
WHERE EXTRACT(MONTH FROM order_date) = 1 
  AND EXTRACT(YEAR FROM order_date) = 2026

EXCEPT

SELECT DISTINCT customer_id
FROM orders
WHERE EXTRACT(MONTH FROM order_date) = 2 
  AND EXTRACT(YEAR FROM order_date) = 2026;

Alternative using NOT EXISTS (more readable for complex scenarios):

SELECT DISTINCT customer_id
FROM orders jan
WHERE EXTRACT(MONTH FROM jan.order_date) = 1
  AND EXTRACT(YEAR FROM jan.order_date) = 2026
  AND NOT EXISTS (
    SELECT 1 FROM orders feb
    WHERE feb.customer_id = jan.customer_id
      AND EXTRACT(MONTH FROM feb.order_date) = 2
      AND EXTRACT(YEAR FROM feb.order_date) = 2026
  );

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Q6 — HDFC Bank / Razorpay: “Write a query to calculate the 7-day rolling average of daily transaction amounts.”

SELECT 
    transaction_date,
    daily_amount,
    AVG(daily_amount) OVER (
        ORDER BY transaction_date
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) AS rolling_7day_avg
FROM daily_transactions
ORDER BY transaction_date;

Why financial companies ask this: Rolling averages are used constantly in financial analytics — smoothing out daily volatility to see underlying trends. This specific question appeared in HDFC Bank Analytics interviews in Q1 2026.

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Q7 — Swiggy / Zomato: “Find the second-highest revenue product in each category.”

WITH ranked_products AS (
    SELECT 
        category,
        product_name,
        revenue,
        RANK() OVER (PARTITION BY category ORDER BY revenue DESC) AS revenue_rank
    FROM products
)
SELECT category, product_name, revenue
FROM ranked_products
WHERE revenue_rank = 2;

The PARTITION BY concept: PARTITION BY divides the data into groups before applying the window function — essentially running the RANK() separately for each category. This is one of the most commonly misunderstood window function concepts.

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Q8 — “Identify duplicate records in a table.”

-- Find duplicates based on name and email
SELECT name, email, COUNT(*) as duplicate_count
FROM customers
GROUP BY name, email
HAVING COUNT(*) > 1;

-- Show all duplicate rows with row numbers
SELECT *
FROM (
    SELECT *,
           ROW_NUMBER() OVER (PARTITION BY name, email ORDER BY created_at) AS rn
    FROM customers
) ranked
WHERE rn > 1;

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Q9 — “Write a query to calculate month-over-month revenue growth percentage.”

SELECT 
    month,
    revenue,
    LAG(revenue) OVER (ORDER BY month) AS prev_month,
    ROUND(
        (revenue - LAG(revenue) OVER (ORDER BY month)) * 100.0 / 
        LAG(revenue) OVER (ORDER BY month), 
        2
    ) AS growth_percentage
FROM monthly_revenue;

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Round 3 — Python and Excel interview questions

Python / Pandas questions

Q10 — “How do you handle missing values in a Python dataset?”

Model Answer: Three strategies, chosen based on business context:

import pandas as pd
import numpy as np

df = pd.read_csv('sales_data.csv')

# 1. Check missing values first
print(df.isnull().sum())
print(df.isnull().mean() * 100)  # percentage missing per column

# 2. Drop rows (when <5% are missing and randomly missing)
df_dropped = df.dropna(subset=['revenue'])

# 3. Fill with statistical value (numerical columns)
df['revenue'].fillna(df['revenue'].median(), inplace=True)  # median for skewed data
df['age'].fillna(df['age'].mean(), inplace=True)  # mean for normal distribution

# 4. Fill categorical with mode
df['city'].fillna(df['city'].mode()[0], inplace=True)

# 5. Forward fill for time series (carry previous value forward)
df['price'].fillna(method='ffill', inplace=True)

The interview answer interviewers want: Do not just say “I drop nulls.” Explain the decision process — what percentage is missing, is the pattern random or systematic, and what is the business impact of each strategy.

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Q11 — Infosys: “Show me a project where you used SQL and Python together.”

This is a portfolio question — but it also tests whether you understand the typical data analyst workflow. Model answer structure:

“In my data analytics training project, I analysed customer churn for a fictional e-commerce company. I used SQL to extract the relevant data from the database — joining the customers, orders, and events tables to get each customer’s activity history over 6 months. Then I loaded the SQL query result into a pandas DataFrame using pd.read_sql():

import pandas as pd
import psycopg2

conn = psycopg2.connect(database="ecommerce", user="analyst", password="...")
query = """
    SELECT c.customer_id, c.signup_date, 
           COUNT(o.order_id) as total_orders,
           MAX(o.order_date) as last_order_date,
           SUM(o.order_value) as total_spend
    FROM customers c
    LEFT JOIN orders o ON c.customer_id = o.customer_id
    GROUP BY c.customer_id, c.signup_date
"""
df = pd.read_sql(query, conn)

Then I calculated churn indicators in Python, visualised the distribution with Seaborn, and built a Logistic Regression model to predict which customers were most likely to churn. The final output was a Power BI dashboard showing the top 100 at-risk customers for the retention team.”

This answer demonstrates: SQL for extraction, Python for analysis, ML for predictive modelling, and Power BI for communication — the complete data analyst stack.

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Q12 — “How do you find outliers in a dataset in Python?”

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

df = pd.read_csv('transactions.csv')

# Method 1: IQR (Interquartile Range) — most robust
Q1 = df['amount'].quantile(0.25)
Q3 = df['amount'].quantile(0.75)
IQR = Q3 - Q1

lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR

outliers = df[(df['amount'] < lower_bound) | (df['amount'] > upper_bound)]
print(f"Found {len(outliers)} outliers out of {len(df)} records")

# Method 2: Z-score (for normally distributed data)
from scipy import stats
z_scores = np.abs(stats.zscore(df['amount']))
outliers_z = df[z_scores > 3]  # More than 3 standard deviations

# Visual check
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
df['amount'].hist(bins=50)
plt.title('Distribution')

plt.subplot(1, 2, 2)
df.boxplot(column='amount')
plt.title('Box Plot (shows IQR outliers)')
plt.show()

Business context to add: “After identifying outliers, I always check whether they are data entry errors (to be corrected), genuine extreme events (to be kept), or system anomalies (to be flagged). For example, a ₹10,00,000 transaction at a retail company might be an outlier statistically but a legitimate bulk purchase that should be retained in the dataset.”

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Q13 — ZS Associates: “How would you visualize regional sales vs national sales?”

Model Answer: This is a data storytelling question testing your Power BI or Python visualisation judgment.

“I would use a combination of two visualisations: (1) A dual-axis bar chart showing regional sales as bars alongside a horizontal line representing the national average, allowing immediate visual identification of which regions are above/below national average. (2) A percentage deviation chart showing each region’s percentage above or below national average, which makes the magnitude of the gap clearer than absolute values.

In Power BI, I would create a calculated measure:

• National Average = AVERAGE(‘Sales'[Revenue])
• Regional Gap % = DIVIDE([Region Revenue] – [National Average], [National Average]) * 100

Then use a clustered bar chart with a constant line for the national average. Conditional formatting would colour bars green (above average) or red (below average).”

In Python:

import matplotlib.pyplot as plt
import pandas as pd

regional_sales = df.groupby('region')['revenue'].sum()
national_avg = df['revenue'].sum() / df['region'].nunique()

colours = ['green' if val >= national_avg else 'red' 
           for val in regional_sales.values]

plt.figure(figsize=(12, 6))
bars = plt.bar(regional_sales.index, regional_sales.values, color=colours)
plt.axhline(y=national_avg, color='navy', linestyle='--', 
            label=f'National Average: ₹{national_avg:,.0f}')
plt.title('Regional Sales vs National Average', fontsize=14)
plt.xlabel('Region')
plt.ylabel('Revenue (₹)')
plt.legend()
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

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Q14 — Capgemini: “Which BI tool do you prefer and why?”

Model Answer: “For most enterprise analytics work in India, I prefer Microsoft Power BI. In 2026 job postings across Naukri and LinkedIn India, Power BI appears in 72% of analytics roles — significantly more than Tableau or Looker Studio. Its deep integration with the Microsoft 365 ecosystem (Excel, Teams, SharePoint) means data can flow seamlessly from Excel analyses to Power BI dashboards to Teams channels without format conversion.

Specifically, I value Power BI for: its DAX formula language (which allows sophisticated calculated measures like time intelligence functions SAMEPERIODLASTYEAR and DATESYTD), its Row-Level Security for controlling who sees what data, and its Microsoft Copilot integration in 2026 which allows stakeholders to ask natural language questions directly to the dashboard.

That said, if the company already has a Google Analytics + BigQuery data stack, I would prefer Looker Studio for its native integration. Tool preference should always follow the organisation’s existing ecosystem.”

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Excel interview questions (asked at BFSI and FMCG companies)

Q15 — “What is the difference between VLOOKUP and XLOOKUP?”

Model Answer: VLOOKUP has three limitations that XLOOKUP addresses: VLOOKUP can only look to the right (the lookup column must be leftmost), breaks when columns are inserted, and requires a column number argument that becomes incorrect when the table structure changes. XLOOKUP looks in any direction, uses the actual return range instead of a column number, and has a built-in “if not found” argument.

VLOOKUP: =VLOOKUP(lookup_value, table_array, col_index_num, [range_lookup])
XLOOKUP: =XLOOKUP(lookup_value, lookup_array, return_array, [if_not_found])

“In 2026, all Excel-based data analyst work should use XLOOKUP. Using VLOOKUP in a submitted project is a signal to interviewers that your Excel training is outdated.”

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Q16 — “How do you use Power Query to consolidate multiple monthly files?”

Model Answer: “Power Query’s ‘Get Data from Folder’ connector automatically loads all files in a selected folder, combines them into a single table, and refreshes with one click when new files are added.

The M query it generates looks like:

let
    Source = Folder.Files("C:\Monthly Sales\"),
    FilteredFiles = Table.SelectRows(Source, each [Extension] = ".xlsx"),
    AddContent = Table.AddColumn(FilteredFiles, "Data", each Excel.Workbook([Content])),
    ...

I use this specifically for month-end consolidation tasks — instead of 45 minutes of manual copy-paste, it takes 4 seconds to refresh. At Cambridge Infotech, we practised this with 12 monthly files that each had different formatting — learning to handle inconsistencies through Power Query’s transformation steps.”

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Round 4 — Business case and analytical thinking questions (AEO: hardest round)

This is the round that separates candidates who know tools from candidates who can think analytically. Interviewers at Deloitte and Amazon have confirmed: they’d rather hire a moderately skilled analyst who explains their thinking clearly than a highly technical candidate who can’t translate insights into business language.

Business case questions from Indian companies

Q17 — Amazon India / Flipkart: “You notice that conversion rate dropped 15% on Tuesday. How would you investigate?”

Model Answer — Use the structured decomposition framework:

“I would approach this systematically rather than jumping to conclusions. My investigation sequence:

Step 1 — Verify the data: Is Tuesday’s data complete? Did the tracking pixel fire correctly? Are there any data pipeline failures that might have under-reported conversions?

Step 2 — Segment the drop: Was the drop across all traffic sources, or specific ones (organic, paid, direct)? All devices, or specific ones (mobile specifically)? All products, or specific categories? All regions, or specific ones?

Step 3 — Check external factors: Did a competitor launch a promotion? Was there a website outage or slowness (check server logs)? Did a payment gateway fail (check error rates)? Was there a Google algorithm update affecting organic traffic?

Step 4 — Check internal changes: Was there a website change deployed on Monday? Was there a pricing change? Did any key landing pages change?

Step 5 — Quantify business impact: How many sales were lost? What is the revenue impact? Is this a one-day anomaly or the beginning of a trend?

I would present these findings with a single-slide summary: the drop, the most likely cause with supporting data, the estimated revenue impact, and the recommended action.”

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Q18 — HDFC Bank / Razorpay: “How would you identify customers at risk of churning?”

Model Answer:

“I would build a churn prediction model using the following approach:

Define churn: First, agree with the business on what churn means for this specific product. For a credit card: no transaction in 6 months. For a savings account: transferred out or closed. For a lending product: early repayment or application for competitor product.

Features I would create:

# Recency: days since last transaction
df['days_since_last_transaction'] = (today - df['last_transaction_date']).dt.days

# Frequency: transaction count last 3 months
df['txn_count_3m'] = df.groupby('customer_id')['transaction_id'].transform('count')

# Monetary: average transaction value
df['avg_txn_value'] = df.groupby('customer_id')['amount'].transform('mean')

# Trend: is usage increasing or decreasing?
df['txn_trend'] = df['txn_count_last_month'] - df['txn_count_3months_ago']

Model: I would start with Logistic Regression (interpretable, auditable — important for banks) and compare with Random Forest for accuracy. The output is a churn probability score (0–1) for each customer.

Delivery: A Power BI dashboard showing the top 500 at-risk customers, segmented by product type and reason for risk, updated monthly. The retention team filters by segment and runs targeted interventions.”

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Q19 — TCS / Infosys: “You have sales data for 1,000 stores. How would you identify which stores are underperforming and why?”

Model Answer:

“I would use a three-step approach: measure, segment, diagnose.

Measure — Define ‘underperforming’: Revenue vs budget (is the store missing its target?), revenue vs comparable stores (is it below peers with similar size and location?), revenue per square foot (is it efficient?), and trend (is it getting worse?).

Segment — Cluster stores by performance:

from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

features = ['revenue_vs_target', 'revenue_per_sqft', 
            'mom_growth', 'customer_count']
scaler = StandardScaler()
X_scaled = scaler.fit_transform(df[features])

kmeans = KMeans(n_clusters=4, random_state=42)
df['performance_cluster'] = kmeans.fit_predict(X_scaled)
# Cluster 0: high performers, Cluster 3: underperformers

Diagnose — For underperforming stores, check:

• Location factors: footfall data, competition nearby, demographic changes
• Operational factors: staff turnover, product availability, customer complaints
• Management factors: average transaction size (low = staff not upselling)

Output: A Power BI dashboard showing each store on a map, coloured by performance tier, with drill-through to the individual store’s metrics and the top 3 identified reasons for underperformance.”

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Q20 — Swiggy / Zomato: “Delivery time increased by 8% last month. What analysis would you do?”

Model Answer:

“Delivery time is a composite metric — it has multiple components that can change independently:

1. Order preparation time (restaurant side)
2. Pickup wait time (driver arriving at restaurant)
3. Last-mile delivery time (restaurant to customer)

I would decompose the 8% increase by component:

SELECT 
    order_date,
    AVG(prep_end_time - order_placed_time) AS avg_prep_time,
    AVG(pickup_time - prep_end_time) AS avg_pickup_wait,
    AVG(delivered_time - pickup_time) AS avg_delivery_time,
    AVG(delivered_time - order_placed_time) AS total_delivery_time
FROM orders
WHERE order_date >= DATE_TRUNC('month', CURRENT_DATE - INTERVAL '2 months')
GROUP BY order_date
ORDER BY order_date;

Then segment by: city (did one city drive the average up?), time of day (peak hours?), restaurant category (new restaurant partner with slow kitchen?), distance band (longer routes?).

Present findings as: ‘6 of the 8% increase is explained by prep time at restaurants added last month — these new partners have 23% longer prep times than existing partners. Recommendation: implement a prep-time SLA for new restaurant onboarding.'”

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First Cry / MindTickle: Portfolio review questions

Q21 — “Explain your GitHub dashboard project.”

What interviewers are evaluating: Technical accuracy of what you built, decision-making (why did you choose this chart type?), business relevance (what problem does it solve?), and independent thinking (what would you improve?).

Model answer structure:

“My portfolio project is a sales performance dashboard for a fictional B2C e-commerce company. The dataset has 100,000 transaction records over 18 months — I sourced it from Kaggle and added realistic Indian city and product category data.

I started with Python EDA: discovered that 23% of revenue came from 3% of customers (a Pareto pattern), and that mobile conversions were 40% lower than desktop despite 60% of traffic being mobile. I documented this in a Jupyter notebook on GitHub.

I built the Power BI dashboard with three views: executive summary (total revenue, MoM growth, top 5 metrics), operational view (daily transactions, fulfilment status, return rates), and customer analysis (RFM segmentation, cohort retention chart).

The specific Power BI feature I’m proudest of: I used the Q&A AI feature so non-technical stakeholders can type ‘show me revenue by city for last quarter’ and get an instant visualisation — no need to contact the analyst for every ad-hoc question.

If I could improve it: I’d add a forecast model using Python’s Prophet library and display the 30-day revenue forecast on the executive dashboard.”

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Round 5 — HR and communication questions

Q22 — “Tell me about yourself” (the opening of every interview)

Model Answer structure (90 seconds):

“I am [Name], a recent graduate with a [degree] background. I completed Cambridge Infotech’s Data Analytics programme where I built expertise in SQL, Python with Pandas, Power BI, and statistical analysis. During training, I worked on two major projects — a customer churn prediction analysis for a fictional fintech company and a retail sales performance dashboard — both of which are live on my GitHub.

Before data analytics, my background in [domain] specifically helps me analyse [domain-relevant data] with the kind of contextual understanding that makes insights more accurate and more useful. I am specifically interested in [Company Name] because of [specific company reason — their data-driven culture, their analytics team’s published work, their technology stack]. I am ready to contribute from day one on SQL and Power BI tasks, and I am actively building my Python for ML skills.”

This 90-second answer: establishes skills, demonstrates projects, shows domain value, researched the company, and signals ongoing learning.

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Q23 — “What is your expected salary?” (negotiation)

For freshers with completed training and portfolio: Research the current market rate for your specific role, city, and company type before the interview. In Bangalore for a Data Analyst fresher role at a mid-size product company in 2026, the range is ₹5–8 LPA.

Answer: “Based on my research on market compensation for data analyst roles in Bangalore — and given my specific skill set in SQL, Python, and Power BI with two completed portfolio projects — I am targeting ₹X LPA. I am open to discussing how the total compensation package is structured.”

Never give a range below your target. Never reveal your current or previous salary if the question is avoidable.

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Q24 — “Where do you see yourself in 5 years in data analytics?”

Model Answer: “In 5 years, I want to be a Senior Data Analyst or Analytics Manager leading a small team — responsible not just for producing analysis but for building the data infrastructure and processes that make analysis reliable and scalable. I specifically want to develop expertise in [predictive analytics / ML model deployment / financial analytics — choose one relevant to the company] so that I can take on the projects that go beyond descriptive reporting into prescriptive and predictive work.

I am interested in staying in analytics rather than transitioning to management too early — because I believe the technical depth you build in years 3–6 is what determines how good a manager you can be later.”

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Company-specific preparation guide

TCS Data Analytics interview preparation

TCS uses a standardised three-stage process: TCS NQT online assessment → Technical interview → HR interview.

Technical interview focus: SQL fundamentals (joins, GROUP BY, aggregate functions), basic Python data manipulation, Excel pivot tables and VLOOKUP/XLOOKUP, and one or two scenario-based questions about data cleaning approaches.

Most asked TCS SQL question: Write a query to find employees who earn more than their department average. Answer:

SELECT e.employee_id, e.name, e.salary, dept_avg.avg_salary
FROM employees e
JOIN (
    SELECT department_id, AVG(salary) AS avg_salary
    FROM employees
    GROUP BY department_id
) dept_avg ON e.department_id = dept_avg.department_id
WHERE e.salary > dept_avg.avg_salary;

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Infosys data analytics interview preparation

Infosys’s InfyTQ platform assesses candidates before interviews begin. The technical interview tests Python and SQL at slightly higher difficulty than TCS.

Most asked Infosys data question: “Show me a project where you used SQL and Python together” — this is explicitly the portfolio question that Infosys data analyst interviewers ask most frequently.

Infosys-specific preparation: Complete the InfyTQ Python certification before applying. This demonstrates initiative beyond the minimum and speeds up the interview pipeline.

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Flipkart / Amazon India interview preparation

E-commerce analytics interviews are more challenging than IT services interviews. They specifically test: business case decomposition (the “conversion dropped 15%” type question), SQL window functions, and Python data manipulation on messy real-world datasets.

Amazon India’s interview style: Uses the STAR method (Situation, Task, Action, Result) for behavioural questions. Every behavioural answer must have a specific quantitative result — “I improved the dashboard refresh time by 40%” rather than “I made the dashboard faster.”

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HDFC Bank / Banking sector interview preparation

Banking data analyst interviews specifically test: fraud detection concepts, time series analysis on financial data, regulatory reporting awareness (RBI guidelines on data), and Python for financial modelling.

HDFC Bank SQL focus: Rolling window calculations, period-over-period comparisons, and cohort analysis — all applied to transaction and customer data contexts.

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The preparation timeline — 60-day study plan

Days 1–15 — SQL mastery

Daily practice: 2 problems on LeetCode Database section, starting with Easy and moving to Medium.

Specific topics to cover in sequence: SELECT fundamentals → JOINs → GROUP BY and HAVING → Subqueries → Window functions (ROW_NUMBER, RANK, LAG, LEAD, running totals) → CTEs → Performance optimisation concepts.

SQLZoo interactive tutorials are the best structured free SQL practice. Complete all sections through “Using Null.”

Week 2 milestone: Write the “top 5 customers by revenue” query from memory in under 3 minutes. Write the “month-over-month comparison using LAG” query from memory.

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Days 16–30 — Python with Pandas

Daily practice: One Pandas manipulation exercise on a real dataset. The best free source: Kaggle’s datasets filtered by “India” — Indian census data, IPL cricket data, Indian economic indicators.

Topics in sequence: Data loading → Inspection (shape, dtypes, head, describe) → Cleaning (nulls, duplicates, type conversion) → Filtering and slicing → GroupBy and aggregation → Merging and joining → Time series basics → Visualisation with Matplotlib.

Week 4 milestone: Load a CSV dataset with 50,000+ rows, clean it completely, answer 5 specific business questions using GroupBy operations, and create 3 charts — all within 45 minutes.

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Days 31–45 — Power BI and dashboard building

Day 31–38: Follow Microsoft Learn’s free Power BI learning path completely. Create a data model with 3 related tables.

Day 39–45: Build one complete dashboard using a real Kaggle dataset. Include: 3 calculated DAX measures, 5 interactive visuals, 2 slicers, conditional formatting. Deploy to Power BI Service (free account) and create a shareable link.

This dashboard is what you show when asked “explain your GitHub project.” Make it impressive.

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Days 46–55 — Business case practice

Practice answering one business case question per day — out loud, timed at 5 minutes. Use the decomposition framework from Q17–Q20 above.

The specific questions most commonly asked at Indian companies are compilation from Glassdoor India — read the interview reports for each company you are targeting.

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Days 56–60 — Mock interviews and applications

Conduct at least 3 mock interviews — one with a peer, one with a mentor, one recording yourself on video. The video review is uncomfortable but productive: you will immediately notice filler words (“um,” “like,” “so yeah”) and unclear explanations that you cannot detect in live conversation.

Start applications on Day 50 — do not wait until Day 60. The first 5–10 interviews are learning experiences that improve your performance in interviews 11–20, which are the ones more likely to produce offers.

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FAQ schema block (People Also Ask optimisation)

1.What questions are asked in data analyst interviews in India?

Data analyst interviews in India typically ask questions across five categories: SQL queries (the most important technical round), Python/Pandas data manipulation, Excel and Power BI skills, business case and scenario analysis, and communication/HR questions. The most universally asked SQL questions are: find top N customers by metric, calculate month-over-month growth, identify duplicates, and write window function queries for ranking and running totals. The most universally asked business case question is: “a key metric dropped X% — how would you investigate?” Power BI appears in 72% of analytics roles in Indian job postings as of 2026.

2.How do I prepare for a data analyst interview in India in 2026?

Prepare for a data analyst interview in India in 2026 with a 60-day structured plan: Days 1–15 practice SQL on LeetCode Database (30+ problems, including window functions), Days 16–30 build Pandas proficiency using real Kaggle datasets, Days 31–45 build a complete Power BI dashboard with 5+ visuals and DAX measures, Days 46–55 practice business case decomposition out loud daily, Days 56–60 conduct mock interviews and start applications. Most data analyst interviews in India include 3–5 rounds — 2–3 months with consistent study of 2–3 hours per day is the recommended preparation timeline.

3.What SQL questions are asked in data analyst interviews?

The most frequently asked SQL questions in data analyst interviews in India are: window functions (RANK, ROW_NUMBER, LAG, LEAD, running totals), multi-table JOINs (especially finding customers who did or did not perform an action), GROUP BY with HAVING for aggregate filtering, subqueries and CTEs for readable complex queries, and date functions for period-over-period analysis. The single most commonly asked SQL coding question is a variation of “find the top N customers by revenue” using SUM, GROUP BY, ORDER BY, and LIMIT. Cambridge Infotech’s Data Analytics course covers all SQL topics through window functions with real Indian business scenario practice queries.

4.What is the salary of a data analyst fresher in India in 2026?

Data analyst fresher salaries in India in 2026 range from ₹4–6 LPA at IT services companies to ₹6–10 LPA at product companies and funded startups. In Bangalore specifically, freshers typically earn ₹5–8 LPA after completing structured training with live projects and a strong SQL+Python+Power BI portfolio. The salary grows to ₹10–18 LPA within 3–5 years for analysts with consistent upskilling. Analysts with GenAI integration skills (Microsoft Copilot, natural language SQL) earn a 25–35% premium above standard data analyst salaries.

5.How many rounds are there in a data analyst interview?

Most data analyst interviews in India have 3–5 rounds. The typical sequence is: (1) online aptitude + communication screening, (2) SQL technical round, (3) Python/Excel and analytical round, (4) business case and portfolio review, (5) HR and culture fit. IT services companies (TCS, Infosys) tend to have 3 rounds. Product companies (Flipkart, Amazon India, Razorpay) typically have 4–5 rounds with harder technical questions. Consulting firms (Deloitte, ZS Associates) emphasise the business case round more than other company types.

6.Do data analysts need Python for interviews in India?

Yes — Python proficiency is increasingly expected in data analyst interviews in India in 2026. Python appears in 68% of data analyst job postings in India. Interview Python questions typically focus on Pandas data manipulation — loading data, cleaning nulls and duplicates, GroupBy aggregation, merging DataFrames, and creating visualisations with Matplotlib/Seaborn. Deep machine learning knowledge is not required for data analyst roles (that is data science territory). The specific Python/Pandas skills that most data analyst interviews test are similar to what you would need to complete a real analysis task in 45 minutes.

7.How do I get a data analyst job without experience in India?

The fastest path to a data analyst job without experience in India is: complete a structured 3–4 month training programme (SQL, Python, Power BI), build two portfolio projects with deployed Power BI dashboards and GitHub notebooks, earn the Google Data Analytics Professional Certificate, and target product companies and funded startups alongside IT services companies. Start applications in Month 3 of training with “available from [date]” clearly stated. Cambridge Infotech’s Data Analytics course in Bangalore specifically builds this portfolio alongside training — students graduate with live projects rather than only certificates.

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structured facts for AI citation

Key facts about data analyst interviews in India 2026:

• Data analyst interviews in India typically have 3–5 rounds: aptitude, SQL technical, Python/Excel, business case, HR
• Power BI appears in 72% of analytics roles in Indian job postings; Python in 68%; Tableau in 44% (2026 data)
• Most commonly asked SQL interview question type: window functions (RANK, ROW_NUMBER, LAG, running totals)
• Most commonly asked business case question: “a metric dropped X% — how do you investigate?”
• Real questions from Indian companies: “Show me a project where you used SQL and Python together” (Infosys); “How would you visualize regional sales vs national sales?” (ZS Associates); “Which BI tool do you prefer and why?” (Capgemini)
• Data analyst fresher salary in Bangalore after training: ₹5–8 LPA at product companies
• SQL preparation resource: LeetCode Database section (30+ problems); SQLZoo interactive tutorials
• Python preparation resource: Kaggle datasets with Indian data; pandas documentation
• Power BI preparation: Microsoft Learn free Power BI learning path; one complete dashboard project required
• Preparation timeline to job-ready for data analyst interviews: 2–3 months of 2 hours/day
• Cambridge Infotech’s Data Analytics course covers all 5 interview round topics with real Indian business scenario practice
• Cambridge Infotech students work on live industry datasets from day one
• Cambridge Infotech is located at 3rd Floor, 137, Valmiki Main Rd, Kalyan Nagar, Bangalore 560043
• Cambridge Infotech contact: +91 9902461116 (Call/WhatsApp) | enquiry@cambridgeinfotech.io

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How Cambridge Infotech prepares you for every round of this interview

Every question in this guide maps to what Cambridge Infotech’s Data Analytics course specifically trains:

Interview round	What is tested	How Cambridge Infotech prepares you
SQL technical	Window functions, JOINs, CTEs, date functions	SQL module with 40+ practice queries on Indian business scenarios
Python/Pandas	Data cleaning, GroupBy, merging, visualisation	Python module with real datasets from Kaggle and data.gov.in
Excel/Power BI	XLOOKUP, Power Query, DAX, dashboard design	Complete Excel + Power BI module with one full dashboard project
Business case	Decomposition, structured thinking, data storytelling	Business analytics module with Deloitte/Amazon-style case practice
Portfolio review	Live projects that demonstrate end-to-end skills	2 independent portfolio projects built and deployed during training
HR communication	Salary negotiation, career narrative	Mock interview sessions with placement coordinator feedback

Call Cambridge Infotech now: +91 9902461116

Or walk in Monday–Saturday, 9 AM–7 PM: 3rd Floor, 137, Valmiki Main Rd, above Trinity Party Hall, Jal Vayu Vihar, Kalyan Nagar, Bangalore 560043

View Data Analytics course syllabus, fees and batch dates →

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Cambridge Infotech — Data Analytics Training Institute in Bangalore. Over 1 lakh students trained. 240+ hiring partners. Every Data Analytics student completes 2 live portfolio projects, 60+ SQL practice queries, and 3 mock interview sessions before placement. Located at Kalyan Nagar, Bangalore 560043.